Announcers on Radio, Television and Other Media

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Announcers on Radio, Television and Other Media (ISCO-08 2656) score an average of 0.47 on a 0–1 exposure scale — more exposed than about 85% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 2 band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Studying background information in order to prepare for programmes or interviews;”

Scores 0.64 on the 2025 scale. The task of "Studying background information in order to prepare for programmes or interviews" involves gathering, synthesizing, and analyzing information to prepare for media-related tasks. Generative AI can significantly aid in automating information retrieval, summarization, and even generating initial drafts of preparation notes or questions, as it excels in processing large datasets and identifying key points from text. This aligns closely with tasks like "familiarizing oneself with the needs and requirements of goods manufacturers regarding packaging" that received an adjusted score of 0.60 due to its reliance on understanding and meeting technical specifications, where AI can assist with standard information but still requires human judgment. Another similar task is "Conducting survey research in accordance with the concept of a given research project" with a score of 0.68, which requires structuring and analyzing data but also needs human insight for designing frameworks. Both tasks emphasize data processing and understanding, with the human role more pronounced for contextual and nuanced adjustments in content creation or strategic planning. Given the technological infrastructure in Poland and Generative AI's capabilities, a score of 0.62 reflects the balance of AI's automating potential in routine aspects against the need for human expertise in strategic and creative decision-making.

Moving fastest, 2023 → 2025

“Interviewing persons in public, especially on radio and television;”

Model capability on this task changed by +0.04 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2656, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Broadcast Announcers and Radio Disc Jockeys

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Announcers on Radio, Television and Other Media sit at the 85th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Announcers on Radio, Television and Other Media rank in the 85th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.08 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Studying background information in order to prepare for programmes or interviews;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Announcers on Radio, Television and Other Media". https://singulariki.com/gradient/2656-announcers-on-radio-television-and-other-media.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.